{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyCaret 2 Classification Example\n",
    "This notebook is created using PyCaret 2.0. Last updated : 28-07-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pycaret-nightly-0.39\n"
     ]
    }
   ],
   "source": [
    "# check version\n",
    "from pycaret.utils import version\n",
    "version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Data Types</th>\n",
       "      <th>Default Task</th>\n",
       "      <th>Target Variable</th>\n",
       "      <th># Instances</th>\n",
       "      <th># Attributes</th>\n",
       "      <th>Missing Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anomaly</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Anomaly Detection</td>\n",
       "      <td>None</td>\n",
       "      <td>1000</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>france</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Association Rule Mining</td>\n",
       "      <td>InvoiceNo, Description</td>\n",
       "      <td>8557</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>germany</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Association Rule Mining</td>\n",
       "      <td>InvoiceNo, Description</td>\n",
       "      <td>9495</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bank</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>deposit</td>\n",
       "      <td>45211</td>\n",
       "      <td>17</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blood</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>748</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cancer</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>683</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>credit</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>default</td>\n",
       "      <td>24000</td>\n",
       "      <td>24</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diabetes</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class variable</td>\n",
       "      <td>768</td>\n",
       "      <td>9</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>electrical_grid</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>stabf</td>\n",
       "      <td>10000</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>employee</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>left</td>\n",
       "      <td>14999</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>heart</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>DEATH</td>\n",
       "      <td>200</td>\n",
       "      <td>16</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>heart_disease</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Disease</td>\n",
       "      <td>270</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hepatitis</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>154</td>\n",
       "      <td>32</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>income</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>income &gt;50K</td>\n",
       "      <td>32561</td>\n",
       "      <td>14</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>juice</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>1070</td>\n",
       "      <td>15</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nba</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>TARGET_5Yrs</td>\n",
       "      <td>1340</td>\n",
       "      <td>21</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>wine</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>type</td>\n",
       "      <td>6498</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>telescope</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>19020</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>glass</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Type</td>\n",
       "      <td>214</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>iris</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>species</td>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>poker</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>CLASS</td>\n",
       "      <td>100000</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>questions</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Next_Question</td>\n",
       "      <td>499</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>satellite</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Class</td>\n",
       "      <td>6435</td>\n",
       "      <td>37</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>asia_gdp</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>elections</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>3195</td>\n",
       "      <td>54</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>facebook</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>7050</td>\n",
       "      <td>12</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ipl</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>153</td>\n",
       "      <td>25</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>jewellery</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>505</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>mice</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>1080</td>\n",
       "      <td>82</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>migration</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>233</td>\n",
       "      <td>12</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>perfume</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>pokemon</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>800</td>\n",
       "      <td>13</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>population</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>255</td>\n",
       "      <td>56</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>public_health</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>224</td>\n",
       "      <td>21</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>seeds</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>210</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>wholesale</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>440</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>tweets</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP</td>\n",
       "      <td>tweet</td>\n",
       "      <td>8594</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>amazon</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>reviewText</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>kiva</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>en</td>\n",
       "      <td>6818</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>spx</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Regression</td>\n",
       "      <td>text</td>\n",
       "      <td>874</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>wikipedia</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>Text</td>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>automobile</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>price</td>\n",
       "      <td>202</td>\n",
       "      <td>26</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>bike</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>cnt</td>\n",
       "      <td>17379</td>\n",
       "      <td>15</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>boston</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>medv</td>\n",
       "      <td>506</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>concrete</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>strength</td>\n",
       "      <td>1030</td>\n",
       "      <td>9</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>diamond</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Price</td>\n",
       "      <td>6000</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>energy</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Heating Load / Cooling Load</td>\n",
       "      <td>768</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>forest</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>area</td>\n",
       "      <td>517</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>gold</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Gold_T+22</td>\n",
       "      <td>2558</td>\n",
       "      <td>121</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>house</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>1461</td>\n",
       "      <td>81</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>insurance</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>charges</td>\n",
       "      <td>1338</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>parkinsons</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>PPE</td>\n",
       "      <td>5875</td>\n",
       "      <td>22</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>traffic</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>traffic_volume</td>\n",
       "      <td>48204</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Dataset    Data Types                 Default Task  \\\n",
       "0           anomaly  Multivariate            Anomaly Detection   \n",
       "1            france  Multivariate      Association Rule Mining   \n",
       "2           germany  Multivariate      Association Rule Mining   \n",
       "3              bank  Multivariate      Classification (Binary)   \n",
       "4             blood  Multivariate      Classification (Binary)   \n",
       "5            cancer  Multivariate      Classification (Binary)   \n",
       "6            credit  Multivariate      Classification (Binary)   \n",
       "7          diabetes  Multivariate      Classification (Binary)   \n",
       "8   electrical_grid  Multivariate      Classification (Binary)   \n",
       "9          employee  Multivariate      Classification (Binary)   \n",
       "10            heart  Multivariate      Classification (Binary)   \n",
       "11    heart_disease  Multivariate      Classification (Binary)   \n",
       "12        hepatitis  Multivariate      Classification (Binary)   \n",
       "13           income  Multivariate      Classification (Binary)   \n",
       "14            juice  Multivariate      Classification (Binary)   \n",
       "15              nba  Multivariate      Classification (Binary)   \n",
       "16             wine  Multivariate      Classification (Binary)   \n",
       "17        telescope  Multivariate      Classification (Binary)   \n",
       "18            glass  Multivariate  Classification (Multiclass)   \n",
       "19             iris  Multivariate  Classification (Multiclass)   \n",
       "20            poker  Multivariate  Classification (Multiclass)   \n",
       "21        questions  Multivariate  Classification (Multiclass)   \n",
       "22        satellite  Multivariate  Classification (Multiclass)   \n",
       "23         asia_gdp  Multivariate                   Clustering   \n",
       "24        elections  Multivariate                   Clustering   \n",
       "25         facebook  Multivariate                   Clustering   \n",
       "26              ipl  Multivariate                   Clustering   \n",
       "27        jewellery  Multivariate                   Clustering   \n",
       "28             mice  Multivariate                   Clustering   \n",
       "29        migration  Multivariate                   Clustering   \n",
       "30          perfume  Multivariate                   Clustering   \n",
       "31          pokemon  Multivariate                   Clustering   \n",
       "32       population  Multivariate                   Clustering   \n",
       "33    public_health  Multivariate                   Clustering   \n",
       "34            seeds  Multivariate                   Clustering   \n",
       "35        wholesale  Multivariate                   Clustering   \n",
       "36           tweets          Text                          NLP   \n",
       "37           amazon          Text         NLP / Classification   \n",
       "38             kiva          Text         NLP / Classification   \n",
       "39              spx          Text             NLP / Regression   \n",
       "40        wikipedia          Text         NLP / Classification   \n",
       "41       automobile  Multivariate                   Regression   \n",
       "42             bike  Multivariate                   Regression   \n",
       "43           boston  Multivariate                   Regression   \n",
       "44         concrete  Multivariate                   Regression   \n",
       "45          diamond  Multivariate                   Regression   \n",
       "46           energy  Multivariate                   Regression   \n",
       "47           forest  Multivariate                   Regression   \n",
       "48             gold  Multivariate                   Regression   \n",
       "49            house  Multivariate                   Regression   \n",
       "50        insurance  Multivariate                   Regression   \n",
       "51       parkinsons  Multivariate                   Regression   \n",
       "52          traffic  Multivariate                   Regression   \n",
       "\n",
       "                Target Variable  # Instances  # Attributes Missing Values  \n",
       "0                          None         1000            10              N  \n",
       "1        InvoiceNo, Description         8557             8              N  \n",
       "2        InvoiceNo, Description         9495             8              N  \n",
       "3                       deposit        45211            17              N  \n",
       "4                         Class          748             5              N  \n",
       "5                         Class          683            10              N  \n",
       "6                       default        24000            24              N  \n",
       "7                Class variable          768             9              N  \n",
       "8                         stabf        10000            14              N  \n",
       "9                          left        14999            10              N  \n",
       "10                        DEATH          200            16              N  \n",
       "11                      Disease          270            14              N  \n",
       "12                        Class          154            32              Y  \n",
       "13                  income >50K        32561            14              Y  \n",
       "14                     Purchase         1070            15              N  \n",
       "15                  TARGET_5Yrs         1340            21              N  \n",
       "16                         type         6498            13              N  \n",
       "17                        Class        19020            11              N  \n",
       "18                         Type          214            10              N  \n",
       "19                      species          150             5              N  \n",
       "20                        CLASS       100000            11              N  \n",
       "21                Next_Question          499             4              N  \n",
       "22                        Class         6435            37              N  \n",
       "23                         None           40            11              N  \n",
       "24                         None         3195            54              Y  \n",
       "25                         None         7050            12              N  \n",
       "26                         None          153            25              N  \n",
       "27                         None          505             4              N  \n",
       "28                         None         1080            82              Y  \n",
       "29                         None          233            12              N  \n",
       "30                         None           20            29              N  \n",
       "31                         None          800            13              Y  \n",
       "32                         None          255            56              Y  \n",
       "33                         None          224            21              N  \n",
       "34                         None          210             7              N  \n",
       "35                         None          440             8              N  \n",
       "36                        tweet         8594             2              N  \n",
       "37                   reviewText        20000             2              N  \n",
       "38                           en         6818             7              N  \n",
       "39                         text          874             4              N  \n",
       "40                         Text          500             3              N  \n",
       "41                        price          202            26              Y  \n",
       "42                          cnt        17379            15              N  \n",
       "43                         medv          506            14              N  \n",
       "44                     strength         1030             9              N  \n",
       "45                        Price         6000             8              N  \n",
       "46  Heating Load / Cooling Load          768            10              N  \n",
       "47                         area          517            13              N  \n",
       "48                    Gold_T+22         2558           121              N  \n",
       "49                    SalePrice         1461            81              Y  \n",
       "50                      charges         1338             7              N  \n",
       "51                          PPE         5875            22              N  \n",
       "52               traffic_volume        48204             8              N  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Data Types</th>\n",
       "      <th>Default Task</th>\n",
       "      <th>Target Variable</th>\n",
       "      <th># Instances</th>\n",
       "      <th># Attributes</th>\n",
       "      <th>Missing Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anomaly</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Anomaly Detection</td>\n",
       "      <td>None</td>\n",
       "      <td>1000</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>france</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Association Rule Mining</td>\n",
       "      <td>InvoiceNo, Description</td>\n",
       "      <td>8557</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>germany</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Association Rule Mining</td>\n",
       "      <td>InvoiceNo, Description</td>\n",
       "      <td>9495</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bank</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>deposit</td>\n",
       "      <td>45211</td>\n",
       "      <td>17</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blood</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>748</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cancer</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>683</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>credit</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>default</td>\n",
       "      <td>24000</td>\n",
       "      <td>24</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diabetes</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class variable</td>\n",
       "      <td>768</td>\n",
       "      <td>9</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>electrical_grid</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>stabf</td>\n",
       "      <td>10000</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>employee</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>left</td>\n",
       "      <td>14999</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>heart</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>DEATH</td>\n",
       "      <td>200</td>\n",
       "      <td>16</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>heart_disease</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Disease</td>\n",
       "      <td>270</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hepatitis</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>154</td>\n",
       "      <td>32</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>income</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>income &gt;50K</td>\n",
       "      <td>32561</td>\n",
       "      <td>14</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>juice</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>1070</td>\n",
       "      <td>15</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nba</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>TARGET_5Yrs</td>\n",
       "      <td>1340</td>\n",
       "      <td>21</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>wine</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>type</td>\n",
       "      <td>6498</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>telescope</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Binary)</td>\n",
       "      <td>Class</td>\n",
       "      <td>19020</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>glass</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Type</td>\n",
       "      <td>214</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>iris</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>species</td>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>poker</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>CLASS</td>\n",
       "      <td>100000</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>questions</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Next_Question</td>\n",
       "      <td>499</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>satellite</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification (Multiclass)</td>\n",
       "      <td>Class</td>\n",
       "      <td>6435</td>\n",
       "      <td>37</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>asia_gdp</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>elections</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>3195</td>\n",
       "      <td>54</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>facebook</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>7050</td>\n",
       "      <td>12</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ipl</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>153</td>\n",
       "      <td>25</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>jewellery</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>505</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>mice</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>1080</td>\n",
       "      <td>82</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>migration</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>233</td>\n",
       "      <td>12</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>perfume</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>pokemon</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>800</td>\n",
       "      <td>13</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>population</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>255</td>\n",
       "      <td>56</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>public_health</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>224</td>\n",
       "      <td>21</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>seeds</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>210</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>wholesale</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>None</td>\n",
       "      <td>440</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>tweets</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP</td>\n",
       "      <td>tweet</td>\n",
       "      <td>8594</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>amazon</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>reviewText</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>kiva</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>en</td>\n",
       "      <td>6818</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>spx</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Regression</td>\n",
       "      <td>text</td>\n",
       "      <td>874</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>wikipedia</td>\n",
       "      <td>Text</td>\n",
       "      <td>NLP / Classification</td>\n",
       "      <td>Text</td>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>automobile</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>price</td>\n",
       "      <td>202</td>\n",
       "      <td>26</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>bike</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>cnt</td>\n",
       "      <td>17379</td>\n",
       "      <td>15</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>boston</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>medv</td>\n",
       "      <td>506</td>\n",
       "      <td>14</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>concrete</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>strength</td>\n",
       "      <td>1030</td>\n",
       "      <td>9</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>diamond</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Price</td>\n",
       "      <td>6000</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>energy</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Heating Load / Cooling Load</td>\n",
       "      <td>768</td>\n",
       "      <td>10</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>forest</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>area</td>\n",
       "      <td>517</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>gold</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Gold_T+22</td>\n",
       "      <td>2558</td>\n",
       "      <td>121</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>house</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>1461</td>\n",
       "      <td>81</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>insurance</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>charges</td>\n",
       "      <td>1338</td>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>parkinsons</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>PPE</td>\n",
       "      <td>5875</td>\n",
       "      <td>22</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>traffic</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>traffic_volume</td>\n",
       "      <td>48204</td>\n",
       "      <td>8</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Dataset    Data Types                 Default Task  \\\n",
       "0           anomaly  Multivariate            Anomaly Detection   \n",
       "1            france  Multivariate      Association Rule Mining   \n",
       "2           germany  Multivariate      Association Rule Mining   \n",
       "3              bank  Multivariate      Classification (Binary)   \n",
       "4             blood  Multivariate      Classification (Binary)   \n",
       "5            cancer  Multivariate      Classification (Binary)   \n",
       "6            credit  Multivariate      Classification (Binary)   \n",
       "7          diabetes  Multivariate      Classification (Binary)   \n",
       "8   electrical_grid  Multivariate      Classification (Binary)   \n",
       "9          employee  Multivariate      Classification (Binary)   \n",
       "10            heart  Multivariate      Classification (Binary)   \n",
       "11    heart_disease  Multivariate      Classification (Binary)   \n",
       "12        hepatitis  Multivariate      Classification (Binary)   \n",
       "13           income  Multivariate      Classification (Binary)   \n",
       "14            juice  Multivariate      Classification (Binary)   \n",
       "15              nba  Multivariate      Classification (Binary)   \n",
       "16             wine  Multivariate      Classification (Binary)   \n",
       "17        telescope  Multivariate      Classification (Binary)   \n",
       "18            glass  Multivariate  Classification (Multiclass)   \n",
       "19             iris  Multivariate  Classification (Multiclass)   \n",
       "20            poker  Multivariate  Classification (Multiclass)   \n",
       "21        questions  Multivariate  Classification (Multiclass)   \n",
       "22        satellite  Multivariate  Classification (Multiclass)   \n",
       "23         asia_gdp  Multivariate                   Clustering   \n",
       "24        elections  Multivariate                   Clustering   \n",
       "25         facebook  Multivariate                   Clustering   \n",
       "26              ipl  Multivariate                   Clustering   \n",
       "27        jewellery  Multivariate                   Clustering   \n",
       "28             mice  Multivariate                   Clustering   \n",
       "29        migration  Multivariate                   Clustering   \n",
       "30          perfume  Multivariate                   Clustering   \n",
       "31          pokemon  Multivariate                   Clustering   \n",
       "32       population  Multivariate                   Clustering   \n",
       "33    public_health  Multivariate                   Clustering   \n",
       "34            seeds  Multivariate                   Clustering   \n",
       "35        wholesale  Multivariate                   Clustering   \n",
       "36           tweets          Text                          NLP   \n",
       "37           amazon          Text         NLP / Classification   \n",
       "38             kiva          Text         NLP / Classification   \n",
       "39              spx          Text             NLP / Regression   \n",
       "40        wikipedia          Text         NLP / Classification   \n",
       "41       automobile  Multivariate                   Regression   \n",
       "42             bike  Multivariate                   Regression   \n",
       "43           boston  Multivariate                   Regression   \n",
       "44         concrete  Multivariate                   Regression   \n",
       "45          diamond  Multivariate                   Regression   \n",
       "46           energy  Multivariate                   Regression   \n",
       "47           forest  Multivariate                   Regression   \n",
       "48             gold  Multivariate                   Regression   \n",
       "49            house  Multivariate                   Regression   \n",
       "50        insurance  Multivariate                   Regression   \n",
       "51       parkinsons  Multivariate                   Regression   \n",
       "52          traffic  Multivariate                   Regression   \n",
       "\n",
       "                Target Variable  # Instances  # Attributes Missing Values  \n",
       "0                          None         1000            10              N  \n",
       "1        InvoiceNo, Description         8557             8              N  \n",
       "2        InvoiceNo, Description         9495             8              N  \n",
       "3                       deposit        45211            17              N  \n",
       "4                         Class          748             5              N  \n",
       "5                         Class          683            10              N  \n",
       "6                       default        24000            24              N  \n",
       "7                Class variable          768             9              N  \n",
       "8                         stabf        10000            14              N  \n",
       "9                          left        14999            10              N  \n",
       "10                        DEATH          200            16              N  \n",
       "11                      Disease          270            14              N  \n",
       "12                        Class          154            32              Y  \n",
       "13                  income >50K        32561            14              Y  \n",
       "14                     Purchase         1070            15              N  \n",
       "15                  TARGET_5Yrs         1340            21              N  \n",
       "16                         type         6498            13              N  \n",
       "17                        Class        19020            11              N  \n",
       "18                         Type          214            10              N  \n",
       "19                      species          150             5              N  \n",
       "20                        CLASS       100000            11              N  \n",
       "21                Next_Question          499             4              N  \n",
       "22                        Class         6435            37              N  \n",
       "23                         None           40            11              N  \n",
       "24                         None         3195            54              Y  \n",
       "25                         None         7050            12              N  \n",
       "26                         None          153            25              N  \n",
       "27                         None          505             4              N  \n",
       "28                         None         1080            82              Y  \n",
       "29                         None          233            12              N  \n",
       "30                         None           20            29              N  \n",
       "31                         None          800            13              Y  \n",
       "32                         None          255            56              Y  \n",
       "33                         None          224            21              N  \n",
       "34                         None          210             7              N  \n",
       "35                         None          440             8              N  \n",
       "36                        tweet         8594             2              N  \n",
       "37                   reviewText        20000             2              N  \n",
       "38                           en         6818             7              N  \n",
       "39                         text          874             4              N  \n",
       "40                         Text          500             3              N  \n",
       "41                        price          202            26              Y  \n",
       "42                          cnt        17379            15              N  \n",
       "43                         medv          506            14              N  \n",
       "44                     strength         1030             9              N  \n",
       "45                        Price         6000             8              N  \n",
       "46  Heating Load / Cooling Load          768            10              N  \n",
       "47                         area          517            13              N  \n",
       "48                    Gold_T+22         2558           121              N  \n",
       "49                    SalePrice         1461            81              Y  \n",
       "50                      charges         1338             7              N  \n",
       "51                          PPE         5875            22              N  \n",
       "52               traffic_volume        48204             8              N  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "get_data('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>WeekofPurchase</th>\n",
       "      <th>StoreID</th>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <th>DiscCH</th>\n",
       "      <th>DiscMM</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <th>SpecialMM</th>\n",
       "      <th>LoyalCH</th>\n",
       "      <th>SalePriceMM</th>\n",
       "      <th>SalePriceCH</th>\n",
       "      <th>PriceDiff</th>\n",
       "      <th>Store7</th>\n",
       "      <th>PctDiscMM</th>\n",
       "      <th>PctDiscCH</th>\n",
       "      <th>ListPriceDiff</th>\n",
       "      <th>STORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CH</td>\n",
       "      <td>237</td>\n",
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       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.99</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CH</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>No</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CH</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.40</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.091398</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>MM</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>CH</td>\n",
       "      <td>228</td>\n",
       "      <td>7</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.956535</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id Purchase  WeekofPurchase  StoreID  PriceCH  PriceMM  DiscCH  DiscMM  \\\n",
       "0   1       CH             237        1     1.75     1.99    0.00     0.0   \n",
       "1   2       CH             239        1     1.75     1.99    0.00     0.3   \n",
       "2   3       CH             245        1     1.86     2.09    0.17     0.0   \n",
       "3   4       MM             227        1     1.69     1.69    0.00     0.0   \n",
       "4   5       CH             228        7     1.69     1.69    0.00     0.0   \n",
       "\n",
       "   SpecialCH  SpecialMM   LoyalCH  SalePriceMM  SalePriceCH  PriceDiff Store7  \\\n",
       "0          0          0  0.500000         1.99         1.75       0.24     No   \n",
       "1          0          1  0.600000         1.69         1.75      -0.06     No   \n",
       "2          0          0  0.680000         2.09         1.69       0.40     No   \n",
       "3          0          0  0.400000         1.69         1.69       0.00     No   \n",
       "4          0          0  0.956535         1.69         1.69       0.00    Yes   \n",
       "\n",
       "   PctDiscMM  PctDiscCH  ListPriceDiff  STORE  \n",
       "0   0.000000   0.000000           0.24      1  \n",
       "1   0.150754   0.000000           0.24      1  \n",
       "2   0.000000   0.091398           0.23      1  \n",
       "3   0.000000   0.000000           0.00      1  \n",
       "4   0.000000   0.000000           0.00      0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = get_data('juice')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow1_col0\" class=\"data row1 col0\" >Target Type</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow1_col1\" class=\"data row1 col1\" >Binary</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow2_col0\" class=\"data row2 col0\" >Label Encoded</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow2_col1\" class=\"data row2 col1\" >CH: 0, MM: 1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow3_col1\" class=\"data row3 col1\" >(1070, 19)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow5_col1\" class=\"data row5 col1\" >13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow6_col1\" class=\"data row6 col1\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow10_col1\" class=\"data row10 col1\" >(1070, 19)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow11_col1\" class=\"data row11 col1\" >(748, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow12_col1\" class=\"data row12 col1\" >(322, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow42_col0\" class=\"data row42 col0\" >Fix Imbalance</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83dalevel0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow43_col0\" class=\"data row43 col0\" >Fix Imbalance Method</td>\n",
       "                        <td id=\"T_0230a75a_d1fa_11ea_b457_482ae32b83darow43_col1\" class=\"data row43 col1\" >SMOTE</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3a272470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.classification import *\n",
    "clf1 = setup(data, target = 'Purchase', session_id=123, log_experiment=True, experiment_name='juice-v1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Compare Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            : ;\n",
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       "            : ;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "        }</style><table id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>        <th class=\"col_heading level0 col7\" >MCC</th>        <th class=\"col_heading level0 col8\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8263</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.8959</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.7262</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.8139</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.7644</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.6280</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col7\" class=\"data row0 col7\" >0.6338</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow0_col8\" class=\"data row0 col8\" >0.0580</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col0\" class=\"data row1 col0\" >Linear Discriminant Analysis</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.8263</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.8938</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.7536</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7938</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.7713</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.6317</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col7\" class=\"data row1 col7\" >0.6342</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow1_col8\" class=\"data row1 col8\" >0.0095</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col0\" class=\"data row2 col0\" >Ridge Classifier</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8236</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7499</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.7920</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.7680</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.6262</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col7\" class=\"data row2 col7\" >0.6292</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow2_col8\" class=\"data row2 col8\" >0.0094</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col0\" class=\"data row3 col0\" >Ada Boost Classifier</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.8075</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.8637</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7053</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7837</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.7398</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.5881</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col7\" class=\"data row3 col7\" >0.5924</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow3_col8\" class=\"data row3 col8\" >0.0905</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col0\" class=\"data row4 col0\" >Gradient Boosting Classifier</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8062</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.8869</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7363</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7651</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.7479</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5909</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col7\" class=\"data row4 col7\" >0.5939</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow4_col8\" class=\"data row4 col8\" >0.1508</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col0\" class=\"data row5 col0\" >CatBoost Classifier</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8049</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.8932</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7326</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7629</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.7457</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.5878</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col7\" class=\"data row5 col7\" >0.5899</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow5_col8\" class=\"data row5 col8\" >3.1743</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col0\" class=\"data row6 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.7914</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.8716</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7294</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7367</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.7309</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.5609</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col7\" class=\"data row6 col7\" >0.5633</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow6_col8\" class=\"data row6 col8\" >0.0948</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col0\" class=\"data row7 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.7861</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.8806</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.7053</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.7393</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.7195</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.5471</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col7\" class=\"data row7 col7\" >0.5497</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow7_col8\" class=\"data row7 col8\" >0.1513</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.7621</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.8240</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.6267</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.7397</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.6678</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.4863</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col7\" class=\"data row8 col7\" >0.5000</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow8_col8\" class=\"data row8 col8\" >0.0103</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col0\" class=\"data row9 col0\" >Random Forest Classifier</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.7608</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.8397</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.6674</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.7124</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6848</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.4928</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col7\" class=\"data row9 col7\" >0.4974</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow9_col8\" class=\"data row9 col8\" >0.1152</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col0\" class=\"data row10 col0\" >Decision Tree Classifier</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.7594</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7519</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.6911</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.6970</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.6907</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.4943</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col7\" class=\"data row10 col7\" >0.4975</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow10_col8\" class=\"data row10 col8\" >0.0079</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col0\" class=\"data row11 col0\" >Extra Trees Classifier</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.7433</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.8205</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.6708</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.6758</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.6698</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.4605</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col7\" class=\"data row11 col7\" >0.4638</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow11_col8\" class=\"data row11 col8\" >0.1663</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col1\" class=\"data row12 col1\" >0.7231</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col2\" class=\"data row12 col2\" >0.7683</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col3\" class=\"data row12 col3\" >0.6062</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col4\" class=\"data row12 col4\" >0.6600</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col5\" class=\"data row12 col5\" >0.6287</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col6\" class=\"data row12 col6\" >0.4094</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col7\" class=\"data row12 col7\" >0.4129</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow12_col8\" class=\"data row12 col8\" >0.0059</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col0\" class=\"data row13 col0\" >Naive Bayes</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col1\" class=\"data row13 col1\" >0.7140</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col2\" class=\"data row13 col2\" >0.7952</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col3\" class=\"data row13 col3\" >0.7466</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col4\" class=\"data row13 col4\" >0.6100</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col5\" class=\"data row13 col5\" >0.6708</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col6\" class=\"data row13 col6\" >0.4227</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col7\" class=\"data row13 col7\" >0.4301</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow13_col8\" class=\"data row13 col8\" >0.0039</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col1\" class=\"data row14 col1\" >0.5267</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col3\" class=\"data row14 col3\" >0.4200</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col4\" class=\"data row14 col4\" >0.2409</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col5\" class=\"data row14 col5\" >0.2561</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col6\" class=\"data row14 col6\" >0.0204</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col7\" class=\"data row14 col7\" >0.0299</td>\n",
       "                        <td id=\"T_86c31b86_d1fa_11ea_bece_482ae32b83darow14_col8\" class=\"data row14 col8\" >0.0094</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3c5f0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>        <th class=\"col_heading level0 col7\" >MCC</th>        <th class=\"col_heading level0 col8\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.7861</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.8806</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.7053</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.7393</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.7195</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.5471</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col7\" class=\"data row0 col7\" >0.5497</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow0_col8\" class=\"data row0 col8\" >0.0903</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col0\" class=\"data row1 col0\" >Random Forest Classifier</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.7608</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.8397</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.6674</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7124</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.6848</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.4928</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col7\" class=\"data row1 col7\" >0.4974</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow1_col8\" class=\"data row1 col8\" >0.1133</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col0\" class=\"data row2 col0\" >Decision Tree Classifier</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.7594</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.7519</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.6911</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6970</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.6907</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.4943</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col7\" class=\"data row2 col7\" >0.4975</td>\n",
       "                        <td id=\"T_c82f0990_d1fa_11ea_9343_482ae32b83darow2_col8\" class=\"data row2 col8\" >0.0067</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3a599d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top3 = compare_models(whitelist=['dt','rf','lightgbm'], n_select=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "        }</style><table id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7867</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8501</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6552</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.7600</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.7037</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.5385</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.5421</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8533</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.9355</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.7241</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.8750</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7925</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.6806</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.6879</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7600</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8193</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.6552</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7037</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6786</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.4875</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.4883</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8133</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.9168</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7241</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7778</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7500</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6014</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6023</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.8133</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8838</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.8621</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7143</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7813</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.6209</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.6293</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.8267</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8966</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.6897</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.8333</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7547</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.6225</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.6292</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8267</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.9019</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.7000</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.8400</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7636</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.6286</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.6351</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8400</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.9348</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.7000</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.8750</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.7778</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6552</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6651</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.8243</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8912</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6552</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.8636</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.7451</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.6149</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.6286</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.9189</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.9287</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.8966</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8966</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8966</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.8299</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.8299</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.8263</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8959</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7262</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.8139</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7644</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.6280</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.6338</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0398</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0357</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0808</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0664</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0552</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0854</td>\n",
       "                        <td id=\"T_e5a3b752_d1fa_11ea_b612_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.0854</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3a02ad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col5 {\n",
       "            background:  yellow;\n",
       "        }    #T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col6 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7267</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.7104</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6207</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.6545</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.6372</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.4182</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.4185</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.7267</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.7361</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.7458</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.6286</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.6822</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.4454</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.4505</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7400</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.7382</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.7458</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.6471</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6929</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.4694</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.4730</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.7852</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.7823</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7759</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7031</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7377</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.5566</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.5586</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.7718</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.7610</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.6724</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7222</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.6964</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5140</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5148</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.7501</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.7456</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.7121</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.6711</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.6893</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.4807</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.4831</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >SD</th>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.0241</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.0244</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.0570</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.0355</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.0322</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0493</td>\n",
       "                        <td id=\"T_ed80c29c_d1fa_11ea_887f_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.0491</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3a062ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = create_model('dt', fold = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col2 {\n",
       "            background:  yellow;\n",
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       "            background:  yellow;\n",
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       "            background:  yellow;\n",
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       "            background:  yellow;\n",
       "        }</style><table id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7933</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8651</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6034</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.8140</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.6931</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.5424</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.5562</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.7733</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.8534</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.6949</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.7193</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7069</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.5222</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.5224</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7600</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8167</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.6949</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.6949</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6949</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.4971</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.4971</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8456</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.8904</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7931</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.8070</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.8000</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6743</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6744</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.7785</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8459</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.6379</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7551</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.6916</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5207</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5252</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.7902</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8543</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.6849</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7581</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7173</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.5514</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.5551</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >SD</th>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.0297</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.0241</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.0644</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.0469</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.0417</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0631</td>\n",
       "                        <td id=\"T_f46e7b9a_d1fa_11ea_a4e9_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.0625</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a39e77828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = create_model('rf', fold = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Turbo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>sklearn.linear_model.LogisticRegression</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>sklearn.neighbors.KNeighborsClassifier</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>sklearn.naive_bayes.GaussianNB</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>sklearn.tree.DecisionTreeClassifier</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>SVM - Linear Kernel</td>\n",
       "      <td>sklearn.linear_model.SGDClassifier</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rbfsvm</th>\n",
       "      <td>SVM - Radial Kernel</td>\n",
       "      <td>sklearn.svm.SVC</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gpc</th>\n",
       "      <td>Gaussian Process Classifier</td>\n",
       "      <td>sklearn.gaussian_process.GPC</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>MLP Classifier</td>\n",
       "      <td>sklearn.neural_network.MLPClassifier</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>Ridge Classifier</td>\n",
       "      <td>sklearn.linear_model.RidgeClassifier</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>sklearn.ensemble.RandomForestClassifier</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qda</th>\n",
       "      <td>Quadratic Discriminant Analysis</td>\n",
       "      <td>sklearn.discriminant_analysis.QDA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>Ada Boost Classifier</td>\n",
       "      <td>sklearn.ensemble.AdaBoostClassifier</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gbc</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>sklearn.ensemble.GradientBoostingClassifier</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda</th>\n",
       "      <td>Linear Discriminant Analysis</td>\n",
       "      <td>sklearn.discriminant_analysis.LDA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>et</th>\n",
       "      <td>Extra Trees Classifier</td>\n",
       "      <td>sklearn.ensemble.ExtraTreesClassifier</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>xgboost.readthedocs.io</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>github.com/microsoft/LightGBM</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Classifier</td>\n",
       "      <td>catboost.ai</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Name  \\\n",
       "ID                                          \n",
       "lr                    Logistic Regression   \n",
       "knn                K Neighbors Classifier   \n",
       "nb                            Naive Bayes   \n",
       "dt               Decision Tree Classifier   \n",
       "svm                   SVM - Linear Kernel   \n",
       "rbfsvm                SVM - Radial Kernel   \n",
       "gpc           Gaussian Process Classifier   \n",
       "mlp                        MLP Classifier   \n",
       "ridge                    Ridge Classifier   \n",
       "rf               Random Forest Classifier   \n",
       "qda       Quadratic Discriminant Analysis   \n",
       "ada                  Ada Boost Classifier   \n",
       "gbc          Gradient Boosting Classifier   \n",
       "lda          Linear Discriminant Analysis   \n",
       "et                 Extra Trees Classifier   \n",
       "xgboost         Extreme Gradient Boosting   \n",
       "lightgbm  Light Gradient Boosting Machine   \n",
       "catboost              CatBoost Classifier   \n",
       "\n",
       "                                            Reference  Turbo  \n",
       "ID                                                            \n",
       "lr            sklearn.linear_model.LogisticRegression   True  \n",
       "knn            sklearn.neighbors.KNeighborsClassifier   True  \n",
       "nb                     sklearn.naive_bayes.GaussianNB   True  \n",
       "dt                sklearn.tree.DecisionTreeClassifier   True  \n",
       "svm                sklearn.linear_model.SGDClassifier   True  \n",
       "rbfsvm                                sklearn.svm.SVC  False  \n",
       "gpc                      sklearn.gaussian_process.GPC  False  \n",
       "mlp              sklearn.neural_network.MLPClassifier  False  \n",
       "ridge            sklearn.linear_model.RidgeClassifier   True  \n",
       "rf            sklearn.ensemble.RandomForestClassifier   True  \n",
       "qda                 sklearn.discriminant_analysis.QDA   True  \n",
       "ada               sklearn.ensemble.AdaBoostClassifier   True  \n",
       "gbc       sklearn.ensemble.GradientBoostingClassifier   True  \n",
       "lda                 sklearn.discriminant_analysis.LDA   True  \n",
       "et              sklearn.ensemble.ExtraTreesClassifier   True  \n",
       "xgboost                        xgboost.readthedocs.io   True  \n",
       "lightgbm                github.com/microsoft/LightGBM   True  \n",
       "catboost                                  catboost.ai   True  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rf', 'ada', 'gbc', 'et', 'xgboost', 'lightgbm', 'catboost']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models(type='ensemble').index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensembled_models = compare_models(whitelist = models(type='ensemble').index.tolist(), fold = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Tune Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col2 {\n",
       "            background:  yellow;\n",
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       "            background:  yellow;\n",
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       "            background:  yellow;\n",
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       "        }    #T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col6 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7600</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8328</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.7931</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.6571</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.7188</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.5126</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.5195</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8800</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.9333</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.8621</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.8333</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.8475</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.7486</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.7489</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7600</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8081</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.7241</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.6774</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.7000</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.5004</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.5011</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8267</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.9168</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.8621</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7353</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7937</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6458</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6519</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.8000</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8823</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.8966</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.6842</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7761</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.6012</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.6192</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.8267</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8966</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.7586</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7857</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7719</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.6322</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.6325</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8000</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.9033</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.8000</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7273</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7619</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.5902</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.5922</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8667</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.9341</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.8667</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.8125</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.8387</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.7253</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.7264</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.8784</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8935</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.7931</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.8846</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.8364</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.7400</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.7428</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.8649</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.9287</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.8966</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.7879</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8387</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.7233</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.7277</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.8263</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8929</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.8253</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7585</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7884</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.6420</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.6462</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0435</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0403</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0565</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0706</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0497</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0870</td>\n",
       "                        <td id=\"T_4ca5ed02_d1fb_11ea_9150_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.0860</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3a087f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_lr = tune_model(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col5 {\n",
       "            background:  yellow;\n",
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       "            background:  yellow;\n",
       "        }</style><table id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7467</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8463</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6552</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.6786</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.6667</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.4625</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.4627</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8400</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.9262</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.6897</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.8696</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7692</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.6493</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.6595</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7733</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.7969</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.6552</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7308</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6909</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.5128</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.5147</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8133</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.9160</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7931</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7419</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7667</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6114</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.7733</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8643</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.8276</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.6667</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7385</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5425</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5524</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.7867</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8523</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.6897</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7407</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7143</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.5444</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.5453</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.7733</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.8533</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.7000</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7241</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7119</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.5251</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.5253</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8400</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.9081</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.8000</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.8000</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.8000</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6667</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6667</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.7703</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8590</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6207</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.7500</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.6792</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5028</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.5082</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.9054</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.9184</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.8276</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9231</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8727</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.7978</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.8008</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.8022</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8741</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7259</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7625</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7410</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.5815</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.5848</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0453</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0394</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0742</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0764</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0598</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0952</td>\n",
       "                        <td id=\"T_5b8f592c_d1fb_11ea_a691_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.0957</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a39e77fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_rf = tune_model(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Ensemble Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=123, splitter='best')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col5 {\n",
       "            background:  yellow;\n",
       "        }    #T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col6 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7467</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8223</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6552</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.6786</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.6667</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.4625</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.4627</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8533</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.9040</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.6897</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.9091</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7843</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.6763</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.6912</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7067</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.7864</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.5862</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.6296</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6071</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.3736</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.3742</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.7867</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.8606</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7586</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7097</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7333</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.5559</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.5567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.7200</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8531</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.6897</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.6250</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.6557</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.4207</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.4222</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.7200</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8396</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.5517</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.6667</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.6038</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.3902</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.3944</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8133</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.8604</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.7667</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7667</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7667</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.6111</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.6111</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8133</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.8811</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.7333</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.7857</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.7586</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6067</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6077</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.7838</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8383</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6207</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.7826</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.6923</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5290</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.5375</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.8649</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.8977</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.7586</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8800</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8148</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.7093</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.7142</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.7809</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8543</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.6810</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7434</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7083</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.5335</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.5372</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0534</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0336</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0723</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0939</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0704</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.1127</td>\n",
       "                        <td id=\"T_a98d7478_d1fb_11ea_b029_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.1147</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3e30c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bagged_dt = ensemble_model(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
       "                                                        class_weight=None,\n",
       "                                                        criterion='gini',\n",
       "                                                        max_depth=None,\n",
       "                                                        max_features=None,\n",
       "                                                        max_leaf_nodes=None,\n",
       "                                                        min_impurity_decrease=0.0,\n",
       "                                                        min_impurity_split=None,\n",
       "                                                        min_samples_leaf=1,\n",
       "                                                        min_samples_split=2,\n",
       "                                                        min_weight_fraction_leaf=0.0,\n",
       "                                                        presort='deprecated',\n",
       "                                                        random_state=123,\n",
       "                                                        splitter='best'),\n",
       "                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "                  max_samples=1.0, n_estimators=10, n_jobs=-1, oob_score=False,\n",
       "                  random_state=123, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagged_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col5 {\n",
       "            background:  yellow;\n",
       "        }    #T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col6 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7733</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.7894</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6552</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.7308</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.6909</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.5128</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.5147</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8000</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.8482</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.6897</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.7692</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7273</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.5701</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.5722</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7067</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.7234</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.6552</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.6129</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6333</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.3893</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.3899</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8267</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.8516</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7931</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7667</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7797</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6369</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6371</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.8000</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8557</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.7931</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7188</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7541</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5862</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5883</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.7067</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8362</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.6207</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.6207</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.6207</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.3816</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.3816</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8000</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.8593</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.8000</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7273</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7619</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.5902</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.5922</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8133</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.8733</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.7333</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.7857</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.7586</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6067</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6077</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.7838</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8027</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6897</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.7407</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.7143</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5407</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.5416</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.8378</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.8636</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.7586</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8148</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.7857</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6555</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.6566</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.7848</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8303</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7189</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7288</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7226</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.5470</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.5482</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0429</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0437</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0623</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0625</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0552</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0899</td>\n",
       "                        <td id=\"T_b4a469cc_d1fb_11ea_a70c_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.0902</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3e30c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boosted_dt = ensemble_model(dt, method = 'Boosting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R',\n",
       "                   base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
       "                                                         class_weight=None,\n",
       "                                                         criterion='gini',\n",
       "                                                         max_depth=None,\n",
       "                                                         max_features=None,\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_impurity_split=None,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         presort='deprecated',\n",
       "                                                         random_state=123,\n",
       "                                                         splitter='best'),\n",
       "                   learning_rate=1.0, n_estimators=10, random_state=123)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boosted_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Blend Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col5 {\n",
       "            background:  yellow;\n",
       "        }    #T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col6 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7733</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8276</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6552</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.7308</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.6909</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.5128</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.5147</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8000</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.9052</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.6897</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.7692</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7273</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.5701</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.5722</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.6933</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.7819</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.5862</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.6071</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.5965</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.3493</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.3494</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8133</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.8703</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7931</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7419</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7667</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6114</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.7867</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8722</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.7586</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7097</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7333</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5559</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.7067</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8370</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.6207</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.6207</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.6207</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.3816</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.3816</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8000</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.8719</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.8000</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7273</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7619</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.5902</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.5922</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8133</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.8852</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.7333</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.7857</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.7586</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6067</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6077</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.7703</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8368</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6897</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.7143</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.7018</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5150</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.5152</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.8514</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.8927</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.7586</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8462</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8000</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6823</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.6849</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.7808</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8581</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7085</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7253</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7158</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.5375</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.5387</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0460</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0351</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0688</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0677</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0618</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0981</td>\n",
       "                        <td id=\"T_e7e43ba6_d1fb_11ea_a285_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.0987</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3e1b9278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blender = blend_models(estimator_list = [bagged_dt, boosted_dt], method = 'soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('Ada Boost Classifier_0',\n",
       "                              AdaBoostClassifier(algorithm='SAMME.R',\n",
       "                                                 base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
       "                                                                                       class_weight=None,\n",
       "                                                                                       criterion='gini',\n",
       "                                                                                       max_depth=None,\n",
       "                                                                                       max_features=None,\n",
       "                                                                                       max_leaf_nodes=None,\n",
       "                                                                                       min_impurity_decrease=0.0,\n",
       "                                                                                       min_impurity_split=None,\n",
       "                                                                                       min_samples_leaf=1,\n",
       "                                                                                       min_samples_split=2,\n",
       "                                                                                       min_weight_fraction_leaf=0.0,...\n",
       "                                                                                      min_samples_leaf=1,\n",
       "                                                                                      min_samples_split=2,\n",
       "                                                                                      min_weight_fraction_leaf=0.0,\n",
       "                                                                                      presort='deprecated',\n",
       "                                                                                      random_state=123,\n",
       "                                                                                      splitter='best'),\n",
       "                                                bootstrap=True,\n",
       "                                                bootstrap_features=False,\n",
       "                                                max_features=1.0,\n",
       "                                                max_samples=1.0,\n",
       "                                                n_estimators=10, n_jobs=-1,\n",
       "                                                oob_score=False,\n",
       "                                                random_state=123, verbose=0,\n",
       "                                                warm_start=False))],\n",
       "                 flatten_transform=True, n_jobs=-1, verbose=False,\n",
       "                 voting='soft', weights=None)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Stack Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_50549994_d1fc_11ea_905c_482ae32b83darow10_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_50549994_d1fc_11ea_905c_482ae32b83darow10_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_50549994_d1fc_11ea_905c_482ae32b83darow10_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_50549994_d1fc_11ea_905c_482ae32b83darow10_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_50549994_d1fc_11ea_905c_482ae32b83darow10_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_50549994_d1fc_11ea_905c_482ae32b83darow10_col5 {\n",
       "            background:  yellow;\n",
       "        }    #T_50549994_d1fc_11ea_905c_482ae32b83darow10_col6 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_50549994_d1fc_11ea_905c_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.8267</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8853</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.7241</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.8077</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.7636</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.6274</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.6298</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8000</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.8954</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.6552</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.7917</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7170</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.5645</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.5705</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7867</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8388</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.6897</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7407</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.7143</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.5444</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.5453</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8133</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.8932</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7241</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7778</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7500</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6014</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6023</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.7600</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8677</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.7586</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.6667</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7097</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5066</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5097</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.8000</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8752</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.7241</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7500</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7368</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.5756</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.5759</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8000</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.8856</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.6667</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.8000</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7273</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.5714</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.5774</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8267</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.9174</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.7333</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.8148</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.7719</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6328</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6351</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.8108</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8789</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6552</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.8261</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.7308</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5879</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.5973</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.8243</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.9027</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.6897</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8333</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.7547</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6198</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.6265</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.8048</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8840</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7021</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7809</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7376</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.5832</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.5870</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_50549994_d1fc_11ea_905c_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0196</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0202</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0341</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0477</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0205</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0374</td>\n",
       "                        <td id=\"T_50549994_d1fc_11ea_905c_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.0377</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3ed8b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacker = stack_models(estimator_list = [boosted_dt,bagged_dt,tuned_rf], meta_model=rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Analyze Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "                       oob_score=False, random_state=123, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(rf, plot = 'confusion_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(rf, plot = 'boundary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f56078fe07a49cda703ab9b3f01d2c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Interpret Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "catboost = create_model('catboost', cross_validation=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interpret_model(catboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interpret_model(catboost, plot = 'correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"model output value\"], \"baseValue\": -0.9138822551227155, \"outValue\": -0.251784039262116, \"link\": \"identity\", \"featureNames\": [\"WeekofPurchase\", \"PriceCH\", \"PriceMM\", \"DiscCH\", \"DiscMM\", \"LoyalCH\", \"SalePriceMM\", \"SalePriceCH\", \"PriceDiff\", \"PctDiscMM\", \"PctDiscCH\", \"ListPriceDiff\", \"StoreID_1\", \"StoreID_2\", \"StoreID_3\", \"StoreID_4\", \"StoreID_7\", \"SpecialCH_0\", \"SpecialCH_1\", \"SpecialMM_0\", \"SpecialMM_1\", \"Store7_No\", \"Store7_Yes\", \"STORE_0\", \"STORE_1\", \"STORE_2\", \"STORE_3\", \"STORE_4\"], \"features\": {\"0\": {\"effect\": -0.01339378460723713, \"value\": 251.0}, \"1\": {\"effect\": 0.027070773375812436, \"value\": 1.76}, \"2\": {\"effect\": 0.027720958422195782, \"value\": 2.09}, \"3\": {\"effect\": 0.055677869481645624, \"value\": 0.0}, \"4\": {\"effect\": -0.0740478932122577, \"value\": 0.0}, \"5\": {\"effect\": 1.181988474640421, \"value\": 0.456}, \"6\": {\"effect\": -0.046285091034273196, \"value\": 2.09}, \"7\": {\"effect\": -0.008048929836521859, \"value\": 1.76}, \"8\": {\"effect\": -0.25727517611107836, \"value\": 0.33}, \"9\": {\"effect\": -0.08404656070274878, \"value\": 0.0}, \"10\": {\"effect\": 0.06721820546884015, \"value\": 0.0}, \"11\": {\"effect\": -0.15225399513382398, \"value\": 0.33}, \"12\": {\"effect\": -0.05848162942534209, \"value\": 1.0}, \"13\": {\"effect\": -0.019197888063205357, \"value\": 0.0}, \"14\": {\"effect\": -0.03207965761867298, \"value\": 0.0}, \"15\": {\"effect\": 0.014430503707973047, \"value\": 0.0}, \"16\": {\"effect\": 0.05130417208965439, \"value\": 0.0}, \"17\": {\"effect\": 0.0014822404001534574, \"value\": 1.0}, \"18\": {\"effect\": 0.004152175927949939, \"value\": 0.0}, \"19\": {\"effect\": -0.03911509007531879, \"value\": 1.0}, \"20\": {\"effect\": -0.028294716120652468, \"value\": 0.0}, \"21\": {\"effect\": 0.03334089653618473, \"value\": 1.0}, \"22\": {\"effect\": 0.04514777187530988, \"value\": 0.0}, \"23\": {\"effect\": 0.06084649122274163, \"value\": 0.0}, \"24\": {\"effect\": -0.048256851980327906, \"value\": 1.0}, \"25\": {\"effect\": -0.021004328189065335, \"value\": 0.0}, \"26\": {\"effect\": -0.03614991600862469, \"value\": 0.0}, \"27\": {\"effect\": 0.009649190830867984, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iOQGFE98Z8O4FSRQ3I5LA')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpret_model(catboost, plot = 'reason', observation = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. AutoML()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best = automl(optimize = 'Recall')\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Predict Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col0 {\n",
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       "        }    #T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col2 {\n",
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       "            background:  yellow;\n",
       "        }</style><table id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7867</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8501</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6552</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.7600</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.7037</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.5385</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.5421</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8533</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.9355</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.7241</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.8750</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.7925</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.6806</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.6879</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7600</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8193</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.6552</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7037</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6786</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.4875</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.4883</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.8133</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.9168</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.7241</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7778</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7500</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.6014</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.6023</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.8133</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8838</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.8621</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7143</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7813</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.6209</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.6293</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.8267</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8966</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.6897</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.8333</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7547</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.6225</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.6292</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8267</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.9019</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.7000</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.8400</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7636</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.6286</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.6351</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8400</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.9348</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.7000</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.8750</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.7778</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6552</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6651</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.8243</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8912</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6552</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.8636</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.7451</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.6149</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.6286</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.9189</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.9287</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.8966</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8966</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8966</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.8299</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.8299</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.8263</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8959</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7262</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.8139</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7644</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.6280</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.6338</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0398</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0357</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0808</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0664</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0552</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0854</td>\n",
       "                        <td id=\"T_6b28ddba_d1fd_11ea_8533_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.0854</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3f304438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.8416</td>\n",
       "      <td>0.9048</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.8136</td>\n",
       "      <td>0.7901</td>\n",
       "      <td>0.6631</td>\n",
       "      <td>0.6638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy     AUC  Recall   Prec.      F1   Kappa  \\\n",
       "0  Logistic Regression    0.8416  0.9048   0.768  0.8136  0.7901  0.6631   \n",
       "\n",
       "      MCC  \n",
       "0  0.6638  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WeekofPurchase</th>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <th>DiscCH</th>\n",
       "      <th>DiscMM</th>\n",
       "      <th>LoyalCH</th>\n",
       "      <th>SalePriceMM</th>\n",
       "      <th>SalePriceCH</th>\n",
       "      <th>PriceDiff</th>\n",
       "      <th>PctDiscMM</th>\n",
       "      <th>...</th>\n",
       "      <th>Store7_No</th>\n",
       "      <th>Store7_Yes</th>\n",
       "      <th>STORE_0</th>\n",
       "      <th>STORE_1</th>\n",
       "      <th>STORE_2</th>\n",
       "      <th>STORE_3</th>\n",
       "      <th>STORE_4</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>260.0</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.959305</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.321101</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>229.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.795200</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>2</th>\n",
       "      <td>261.0</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.588965</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.112676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>247.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.003689</td>\n",
       "      <td>2.23</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9127</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>271.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.973612</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.89</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.191388</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WeekofPurchase  PriceCH  PriceMM  DiscCH  DiscMM   LoyalCH  SalePriceMM  \\\n",
       "0           260.0     1.86     2.18     0.0    0.70  0.959305         1.48   \n",
       "1           229.0     1.69     1.69     0.0    0.00  0.795200         1.69   \n",
       "2           261.0     1.86     2.13     0.0    0.24  0.588965         1.89   \n",
       "3           247.0     1.99     2.23     0.0    0.00  0.003689         2.23   \n",
       "4           271.0     1.99     2.09     0.1    0.40  0.973612         1.69   \n",
       "\n",
       "   SalePriceCH  PriceDiff  PctDiscMM  ...  Store7_No  Store7_Yes  STORE_0  \\\n",
       "0         1.86      -0.38   0.321101  ...        1.0         0.0      0.0   \n",
       "1         1.69       0.00   0.000000  ...        1.0         0.0      0.0   \n",
       "2         1.86       0.03   0.112676  ...        0.0         1.0      1.0   \n",
       "3         1.99       0.24   0.000000  ...        1.0         0.0      0.0   \n",
       "4         1.89      -0.20   0.191388  ...        1.0         0.0      0.0   \n",
       "\n",
       "   STORE_1  STORE_2  STORE_3  STORE_4  Purchase  Label   Score  \n",
       "0      0.0      1.0      0.0      0.0         0      0  0.1873  \n",
       "1      0.0      1.0      0.0      0.0         0      0  0.1914  \n",
       "2      0.0      0.0      0.0      0.0         0      0  0.2532  \n",
       "3      0.0      0.0      1.0      0.0         1      1  0.9127  \n",
       "4      0.0      0.0      1.0      0.0         0      0  0.1895  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_holdouts = predict_model(lr)\n",
    "pred_holdouts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>WeekofPurchase</th>\n",
       "      <th>StoreID</th>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <th>DiscCH</th>\n",
       "      <th>DiscMM</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <th>SpecialMM</th>\n",
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       "      <th>SalePriceCH</th>\n",
       "      <th>PriceDiff</th>\n",
       "      <th>Store7</th>\n",
       "      <th>PctDiscMM</th>\n",
       "      <th>PctDiscCH</th>\n",
       "      <th>ListPriceDiff</th>\n",
       "      <th>STORE</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
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       "      <td>0.40</td>\n",
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       "      <td>0.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1282</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>0.000000</td>\n",
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      ],
      "text/plain": [
       "   Id  WeekofPurchase  StoreID  PriceCH  PriceMM  DiscCH  DiscMM  SpecialCH  \\\n",
       "0   1             237        1     1.75     1.99    0.00     0.0          0   \n",
       "1   2             239        1     1.75     1.99    0.00     0.3          0   \n",
       "2   3             245        1     1.86     2.09    0.17     0.0          0   \n",
       "3   4             227        1     1.69     1.69    0.00     0.0          0   \n",
       "4   5             228        7     1.69     1.69    0.00     0.0          0   \n",
       "\n",
       "   SpecialMM   LoyalCH  SalePriceMM  SalePriceCH  PriceDiff Store7  PctDiscMM  \\\n",
       "0          0  0.500000         1.99         1.75       0.24     No   0.000000   \n",
       "1          1  0.600000         1.69         1.75      -0.06     No   0.150754   \n",
       "2          0  0.680000         2.09         1.69       0.40     No   0.000000   \n",
       "3          0  0.400000         1.69         1.69       0.00     No   0.000000   \n",
       "4          0  0.956535         1.69         1.69       0.00    Yes   0.000000   \n",
       "\n",
       "   PctDiscCH  ListPriceDiff  STORE  Label   Score  \n",
       "0   0.000000           0.24      1      0  0.3422  \n",
       "1   0.000000           0.24      1      0  0.4760  \n",
       "2   0.091398           0.23      1      0  0.1282  \n",
       "3   0.000000           0.00      1      1  0.5975  \n",
       "4   0.000000           0.00      0      0  0.0512  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = data.copy()\n",
    "new_data.drop(['Purchase'], axis=1, inplace=True)\n",
    "predict_new = predict_model(lr, data=new_data)\n",
    "predict_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13. Save / Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(lr, model_name='best-model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n",
      "[Pipeline(memory=None,\n",
      "         steps=[('dtypes',\n",
      "                 DataTypes_Auto_infer(categorical_features=[],\n",
      "                                      display_types=True, features_todrop=[],\n",
      "                                      ml_usecase='classification',\n",
      "                                      numerical_features=[], target='Purchase',\n",
      "                                      time_features=[])),\n",
      "                ('imputer',\n",
      "                 Simple_Imputer(categorical_strategy='not_available',\n",
      "                                numeric_strategy='mean',\n",
      "                                target_variable=None)),\n",
      "                ('new_levels1',\n",
      "                 New_Catagorical_L...\n",
      "                ('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\n",
      "                ('P_transform', Empty()), ('pt_target', Empty()),\n",
      "                ('binn', Empty()), ('rem_outliers', Empty()),\n",
      "                ('cluster_all', Empty()), ('dummy', Dummify(target='Purchase')),\n",
      "                ('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\n",
      "                ('feature_select', Empty()), ('fix_multi', Empty()),\n",
      "                ('dfs', Empty()), ('pca', Empty())],\n",
      "         verbose=False), LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=123, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)]\n"
     ]
    }
   ],
   "source": [
    "loaded_bestmodel = load_model('best-model')\n",
    "print(loaded_bestmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"118fef6d-3646-41fc-b40a-92fc524ff367\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"118fef6d-3646-41fc-b40a-92fc524ff367\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      ml_usecase='classification',\n",
       "                                      numerical_features=[], target='Purchase',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_Catagorical_L...\n",
       "                ('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('pt_target', Empty()),\n",
       "                ('binn', Empty()), ('rem_outliers', Empty()),\n",
       "                ('cluster_all', Empty()), ('dummy', Dummify(target='Purchase')),\n",
       "                ('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\n",
       "                ('feature_select', Empty()), ('fix_multi', Empty()),\n",
       "                ('dfs', Empty()), ('pca', Empty())],\n",
       "         verbose=False)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8e04b291-1ce2-4421-b861-25088e111d30\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8e04b291-1ce2-4421-b861-25088e111d30\">DataTypes_Auto_infer</label><div class=\"sk-toggleable__content\"><pre>DataTypes_Auto_infer(ml_usecase='classification', target='Purchase')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"0d383207-83b1-425d-b14a-a06bf4ceac47\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"0d383207-83b1-425d-b14a-a06bf4ceac47\">Simple_Imputer</label><div class=\"sk-toggleable__content\"><pre>Simple_Imputer(categorical_strategy='not_available', numeric_strategy='mean',\n",
       "               target_variable=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f80021ef-edbf-4c3b-8bde-acc63e75ce66\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f80021ef-edbf-4c3b-8bde-acc63e75ce66\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='Purchase')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"215d59ba-d2d2-4147-9cca-ea1f879aede3\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"215d59ba-d2d2-4147-9cca-ea1f879aede3\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9c0f5c0d-7b4d-4c88-9391-66118d78c6ba\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9c0f5c0d-7b4d-4c88-9391-66118d78c6ba\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c341d489-eb6c-4b21-92d5-787a3ef847d4\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c341d489-eb6c-4b21-92d5-787a3ef847d4\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a22a9cda-b72b-44cb-8359-511f5c5d4cf3\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a22a9cda-b72b-44cb-8359-511f5c5d4cf3\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"67a9a50c-a1db-4e12-a430-520c8bd36167\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"67a9a50c-a1db-4e12-a430-520c8bd36167\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='Purchase')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"64776f8d-82f3-49d8-84ef-280d0aceb296\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"64776f8d-82f3-49d8-84ef-280d0aceb296\">Make_Time_Features</label><div class=\"sk-toggleable__content\"><pre>Make_Time_Features(list_of_features=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d61e6d60-bd5f-4cd8-9364-06a3fe02cff1\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d61e6d60-bd5f-4cd8-9364-06a3fe02cff1\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f236eea8-1d8b-4d54-bc54-a98fdd763904\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f236eea8-1d8b-4d54-bc54-a98fdd763904\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8516eae0-4fa1-4686-9d04-33fc27d1641c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8516eae0-4fa1-4686-9d04-33fc27d1641c\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7a011dec-9331-47ab-bf62-7d09902e7055\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"7a011dec-9331-47ab-bf62-7d09902e7055\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"fa99b218-1040-4384-9a5b-8da8e6e6cbcf\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fa99b218-1040-4384-9a5b-8da8e6e6cbcf\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"18b3c120-6153-430a-b7bd-783ea80a73f5\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"18b3c120-6153-430a-b7bd-783ea80a73f5\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b172b311-9382-4603-b5b1-44a0a1798b0d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b172b311-9382-4603-b5b1-44a0a1798b0d\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"beaf48bd-c76f-4f73-a9ed-f3b618de2405\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"beaf48bd-c76f-4f73-a9ed-f3b618de2405\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"30ce6204-e5e4-4b56-9abd-ff97a34accdd\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"30ce6204-e5e4-4b56-9abd-ff97a34accdd\">Dummify</label><div class=\"sk-toggleable__content\"><pre>Dummify(target='Purchase')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f8e9226a-4271-4613-8353-5c12ae30ee89\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f8e9226a-4271-4613-8353-5c12ae30ee89\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8239a17f-d397-4803-b53a-df1f5e0936da\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8239a17f-d397-4803-b53a-df1f5e0936da\">Clean_Colum_Names</label><div class=\"sk-toggleable__content\"><pre>Clean_Colum_Names()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"024be6ae-a12f-4d8b-ad3f-417eb900fc6c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"024be6ae-a12f-4d8b-ad3f-417eb900fc6c\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7807ddf4-c734-48c7-af59-142eb8b96afb\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"7807ddf4-c734-48c7-af59-142eb8b96afb\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"03b8f4c2-3a39-4f2e-b304-9730d5f26d47\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"03b8f4c2-3a39-4f2e-b304-9730d5f26d47\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ba41c9f0-6c2b-4056-b225-d1ad0c18082c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"ba41c9f0-6c2b-4056-b225-d1ad0c18082c\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      ml_usecase='classification',\n",
       "                                      numerical_features=[], target='Purchase',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_Catagorical_L...\n",
       "                ('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('pt_target', Empty()),\n",
       "                ('binn', Empty()), ('rem_outliers', Empty()),\n",
       "                ('cluster_all', Empty()), ('dummy', Dummify(target='Purchase')),\n",
       "                ('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\n",
       "                ('feature_select', Empty()), ('fix_multi', Empty()),\n",
       "                ('dfs', Empty()), ('pca', Empty())],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='diagram')\n",
    "loaded_bestmodel[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='text')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14. Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Succesfully Deployed on AWS S3\n"
     ]
    }
   ],
   "source": [
    "deploy_model(lr, model_name = 'best-aws-123', authentication = {'bucket' : 'pycaret-test'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15. Get Config / Set Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = get_config('X_train')\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.classification import set_config\n",
    "set_config('seed', 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 16. Get System Logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gplearn.genetic import SymbolicClassifier\n",
    "sc = SymbolicClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col5 {\n",
       "            background:  yellow;\n",
       "        }    #T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col6 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow0_col0\" class=\"data row0 col0\" >0.7333</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow0_col1\" class=\"data row0 col1\" >0.8415</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow0_col2\" class=\"data row0 col2\" >0.6207</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.6667</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.6429</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.4305</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.4312</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow1_col0\" class=\"data row1 col0\" >0.8667</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow1_col1\" class=\"data row1 col1\" >0.9352</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow1_col2\" class=\"data row1 col2\" >0.7931</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.8519</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.8214</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.7153</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.7164</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow2_col0\" class=\"data row2 col0\" >0.7867</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow2_col1\" class=\"data row2 col1\" >0.8058</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow2_col2\" class=\"data row2 col2\" >0.6207</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7826</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.6923</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.5323</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.5407</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow3_col0\" class=\"data row3 col0\" >0.7867</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow3_col1\" class=\"data row3 col1\" >0.9160</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow3_col2\" class=\"data row3 col2\" >0.6552</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.7600</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.7037</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.5385</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.5421</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow4_col0\" class=\"data row4 col0\" >0.8000</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow4_col1\" class=\"data row4 col1\" >0.8793</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow4_col2\" class=\"data row4 col2\" >0.8276</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7059</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.7619</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5914</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.5969</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow5_col0\" class=\"data row5 col0\" >0.8267</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow5_col1\" class=\"data row5 col1\" >0.8891</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow5_col2\" class=\"data row5 col2\" >0.6897</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.8333</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.7547</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.6225</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.6292</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow6_col0\" class=\"data row6 col0\" >0.8267</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow6_col1\" class=\"data row6 col1\" >0.8981</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow6_col2\" class=\"data row6 col2\" >0.7667</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7931</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.7797</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.6369</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.6371</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow7_col0\" class=\"data row7 col0\" >0.8533</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow7_col1\" class=\"data row7 col1\" >0.9293</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow7_col2\" class=\"data row7 col2\" >0.7667</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.8519</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.8070</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6893</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.6918</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow8_col0\" class=\"data row8 col0\" >0.7838</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow8_col1\" class=\"data row8 col1\" >0.8774</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow8_col2\" class=\"data row8 col2\" >0.6207</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.7826</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.6923</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5290</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.5375</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow9_col0\" class=\"data row9 col0\" >0.9054</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow9_col1\" class=\"data row9 col1\" >0.9349</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow9_col2\" class=\"data row9 col2\" >0.9310</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8438</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8852</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.8051</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.8079</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col0\" class=\"data row10 col0\" >0.8169</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col1\" class=\"data row10 col1\" >0.8907</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col2\" class=\"data row10 col2\" >0.7292</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7872</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.7541</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.6091</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.6131</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow11_col0\" class=\"data row11 col0\" >0.0469</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow11_col1\" class=\"data row11 col1\" >0.0401</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow11_col2\" class=\"data row11 col2\" >0.0998</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0597</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0691</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.1032</td>\n",
       "                        <td id=\"T_155acf9e_d200_11ea_a9d1_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.1025</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a3f556e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc = create_model(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_system_logs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 17. MLFlow UI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!mlflow ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to generate csv file with experiment logs\n",
    "get_logs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End\n",
    "Thank you. For more information / tutorials on PyCaret, please visit https://www.pycaret.org"
   ]
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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